Three-dimensional diabetic macular edema thickness maps based on fluid segmentation and fovea detection using deep learning  

作　　者：Jing-Jing Xu Yang Zhou Qi-Jie Wei Kang Li Zhen-Ping Li Tian Yu Jian-Chun Zhao Da-Yong Ding Xi-Rong Li Guang-Zhi Wang Hong Dai 

机构地区：[1]School of Medicine,Tsinghua University,Beijing 100084,China [2]Visionary Intelligence Company Limited,Beijing 100872,China [3]Department of Ophthalmology,Beijing Hospital,National Center of Gerontology,Institute of Geriatric Medicine,Chinese Academy of Medical Sciencies,Beijing 100730,China [4]Key Lab of Data Engineering and Knowledge Engineering,Renmin University of China,Beijing 100872,China

出　　处：《International Journal of Ophthalmology(English edition)》2022年第3期495-501,共7页国际眼科杂志（英文版）

摘　　要：AIM: To explore a more accurate quantifying diagnosis method of diabetic macular edema(DME) by displaying detailed 3D morphometry beyond the gold-standard quantification indicator-central retinal thickness(CRT) and apply it in follow-up of DME patients.METHODS: Optical coherence tomography(OCT) scans of 229 eyes from 160 patients were collected.We manually annotated cystoid macular edema(CME), subretinal fluid(SRF) and fovea as ground truths.Deep convolution neural networks(DCNNs) were constructed including U-Net, sASPP, HRNetV2-W48, and HRNetV2-W48+Object-Contextual Representation(OCR) for fluid(CME+SRF) segmentation and fovea detection respectively, based on which the thickness maps of CME, SRF and retina were generated and divided by Early Treatment Diabetic Retinopathy Study(ETDRS) grid.RESULTS: In fluid segmentation, with the best DCNN constructed and loss function, the dice similarity coefficients(DSC) of segmentation reached 0.78(CME), 0.82(SRF), and 0.95(retina).In fovea detection, the average deviation between the predicted fovea and the ground truth reached 145.7±117.8 μm.The generated macular edema thickness maps are able to discover center-involved DME by intuitive morphometry and fluid volume, which is ignored by the traditional definition of CRT>250 μm.Thickness maps could also help to discover fluid above or below the fovea center ignored or underestimated by a single OCT B-scan.CONCLUSION: Compared to the traditional unidimensional indicator-CRT, 3D macular edema thickness maps are able to display more intuitive morphometry and detailed statistics of DME, supporting more accurate diagnoses and follow-up of DME patients.

关 键 词：diabetic macular edema fluid segmentation fovea detection 3D macular edema thickness maps deep learning 

分 类 号：R587.2[医药卫生—内分泌] R774.5[医药卫生—内科学]